An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling

Agrawal, Rohit, Wankhede, Vishal A., Kumar, Anil, Luthra, Sunil, Majumdar, Abhijit and Kazancoglu, Yigit (2021) An exploratory state-of-the-art review of artificial intelligence applications in circular economy using structural topic modeling. Operations Management Research. ISSN 1936-9735 (In Press)

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Abstract / Description

The world is moving into a situation where resource scarcity leads to an increase in material cost. A possible way to deal with the above challenge is to adopt Circular Economy (CE) concepts to make a close loop of material by eliminating industrial or post-consumer wastes. Integration of emerging technologies such as artificial intelligence (AI), machine learning, and big data analytics provides significant support in successfully adopting and implementing CE practices. This study aims to explore the applications of AI techniques in enhancing the adoption and implementation of CE practices. A systematic literature review was performed to analyze the existing scenario and the potential research directions of AI in CE. A collection of 220 articles was shortlisted from the SCOPUS database in the field of AI in CE. A text mining approach, known as Structural Topic Modeling (STM), was used to generate different thematic topics of AI applications in CE. Each generated topic was then discussed with shortlisted articles. Further, a bibliometric study was performed to analyze the research trends in the field of AI applications in CE. A research framework was proposed for AI in CE based on the review conducted, which could help industrial practitioners, and researchers working in this domain. Further, future research propositions on AI in CE were proposed.

Item Type: Article
Uncontrolled Keywords: artificial intelligence; circular economy; emerging technologies; structural topic modeling; text mining; big data analytics
Subjects: 600 Technology > 650 Management & auxiliary services
Department: Guildhall School of Business and Law
Depositing User: Anil Kumar
Date Deposited: 27 Aug 2021 08:25
Last Modified: 08 Sep 2021 13:25
URI: http://repository.londonmet.ac.uk/id/eprint/6939

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